Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-22T23:30:07.110Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting the distribution of canine leishmaniasis in western Europe based on environmental variables

Published online by Cambridge University Press:  14 September 2011

ANA O. FRANCO ,
CLIVE R. DAVIES ,
ADRIAN MYLNE ,
JEAN-PIERRE DEDET ,
MONTSERRAT GÁLLEGO ,
CRISTINA BALLART ,
MARINA GRAMICCIA ,
LUIGI GRADONI ,
RICARDO MOLINA  and
ROSA GÁLVEZ
...Show all authors
ANA O. FRANCO
Affiliation:
Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
CLIVE R. DAVIES
Affiliation:
Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
ADRIAN MYLNE
Affiliation:
Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
JEAN-PIERRE DEDET
Affiliation:
Centre National de Référence des Leishmania, UMR MIVEGEC, Université Montpellier 1/Laboratoire de Parasitologie-Mycologie, CHU de Montpellier, Montpellier, France
MONTSERRAT GÁLLEGO
Affiliation:
Laboratori de Parasitologia, Facultat de Farmàcia, Universitat de Barcelona, Barcelona, Spain
CRISTINA BALLART
Affiliation:
Laboratori de Parasitologia, Facultat de Farmàcia, Universitat de Barcelona, Barcelona, Spain
MARINA GRAMICCIA
Affiliation:
Unit of Vector-borne Diseases and International Health, MIPI Department, Istituto Superiore di Sanità, Rome, Italy
LUIGI GRADONI
Affiliation:
Unit of Vector-borne Diseases and International Health, MIPI Department, Istituto Superiore di Sanità, Rome, Italy
RICARDO MOLINA
Affiliation:
Laboratorio de Referenda de Leishmaniasis, Servicio de Parasitología, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Spain
ROSA GÁLVEZ
Affiliation:
Laboratorio de Referenda de Leishmaniasis, Servicio de Parasitología, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Spain
FRANCISCO MORILLAS-MÁRQUEZ
Affiliation:
Departamento de Parasitología, Facultad de Farmacia, Universidad de Granada, Granada, Spain
SERGIO BARÓN-LÓPEZ
Affiliation:
Departamento de Parasitología, Facultad de Farmacia, Universidad de Granada, Granada, Spain
CARLOS ALVES PIRES
Affiliation:
Unidade de Entomologia Médica/Unidade de Parasitologia e Microbiologia Médica, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal
MARIA ODETE AFONSO
Affiliation:
Unidade de Entomologia Médica/Unidade de Parasitologia e Microbiologia Médica, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal
PAUL D. READY*
Affiliation:
Department of Entomology, Natural History Museum, London, UK
JONATHAN COX
Affiliation:
Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
*
*Corresponding author: Department of Entomology, Natural History Museum, London SW7 5BD, UK. Tel: + 442079425622. Fax: + 442079425229. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

The domestic dog is the reservoir host of Leishmania infantum, the causative agent of zoonotic visceral leishmaniasis endemic in Mediterranean Europe. Targeted control requires predictive risk maps of canine leishmaniasis (CanL), which are now explored. We databased 2187 published and unpublished surveys of CanL in southern Europe. A total of 947 western surveys met inclusion criteria for analysis, including serological identification of infection (504, 369 dogs tested 1971–2006). Seroprevalence was 23 2% overall (median 10%). Logistic regression models within a GIS framework identified the main environmental predictors of CanL seroprevalence in Portugal, Spain, France and Italy, or in France alone. A 10-fold cross-validation approach determined model capacity to predict point-values of seroprevalence and the correct seroprevalence class (<5%, 5–20%, >20%). Both the four-country and France-only models performed reasonably well for predicting correctly the <5% and >20% seroprevalence classes (AUC >0 70). However, the France-only model performed much better for France than the four-country model. The four-country model adequately predicted regions of CanL emergence in northern Italy (<5% seroprevalence). Both models poorly predicted intermediate point seroprevalences (5–20%) within regional foci, because surveys were biased towards known rural foci and Mediterranean bioclimates. Our recommendations for standardizing surveys would permit higher-resolution risk mapping.

Keywords

canine leishmaniasiswestern Mediterraneanpredicting distributionserological recordsseroprevalence
Type
Research Article
Information
Parasitology , Volume 138 , Issue 14 , December 2011 , pp. 1878 - 1891
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Brooker, S., Hay, S. I., Issae, W., Hall, A., Kihamia, C. M., Lwambo, N. J., Wint, W., Rogers, D. J. and Bundy, D. A. (2001). Predicting the distribution of urinary schistosomiasis in Tanzania using satellite sensor data. Tropical Medicine & International Health 6, 998–1007.Google Scholar
Brooker, S., Hay, S. I. and Bundy, D. A. (2002). Tools from ecology: useful for evaluating infection risk models? Trends in Parasitology 18, 70–74.CrossRefGoogle ScholarPubMed
Chamaillé, L., Tran, A., Meunier, A., Bourdoiseau, G., Ready, P. and Dedet, J.-P. (2010). Environmental risk mapping of canine leishmaniasis in France. Parasites & Vectors 3, 31.CrossRefGoogle ScholarPubMed
Clements, A. C., Moyeed, R. and Brooker, S. (2006). Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa. Parasitology 133, 711–719.CrossRefGoogle ScholarPubMed
Dujardin, J. C., Campino, L., Cañavate, C., Dedet, J. P., Gradoni, L., Soteriadou, K., Mazeris, A., Ozbel, Y. and Boelaert, M. (2008). Spread of vector-borne diseases and neglect of Leishmaniasis, Europe. Emerging Infectious Diseases 14, 1013–1018.Google Scholar
Duprey, Z. H., Steurer, F. J., Rooney, J. A., Kirchhoff, L. V., Jackson, J. E., Rowton, E. D. and Schantz, P. M. (2006). Canine visceral leishmaniasis, United States and Canada, 2000–2003. Emerging Infectious Diseases 12, 440–446.Google Scholar
Gálvez, R., Descalzo, M. A., Miró, G., Jiménez, M. I., Martín, O., Dos Santos-Brandao, F., Guerrero, I., Cubero, E. and Molina, R. (2010 a). Seasonal trends and spatial relations between environmental/meteorological factors and leishmaniosis sand fly vector abundances in Central Spain. Acta Tropica 115, 95–102.Google Scholar
Gálvez, R., Miró, G., Descalzo, M. A., Nieto, J., Dado, D., Martín, O., Cubero, E. and Molina, R. (2010 b). Emerging trends in the seroprevalence of canine leishmaniasis in the Madrid region (central Spain). Veterinary Parasitology 169, 327–334.Google Scholar
Gething, P. W., Noor, A. M., Gikandi, P. W., Hay, S. I., Nixon, M. S., Snow, R. W. and Atkinson, P. M. (2008). Developing geostatistical space-time models to predict outpatient treatment burdens from incomplete national data. Geographical Analysis 40, 167–188.CrossRefGoogle ScholarPubMed
Hartemink, N., Vanwambeke, S. O., Heesterbeek, H., Rogers, D., Morley, D., Pesson, B., Davies, C., Mahamdallie, S. and Ready, P. (2011). Integrated mapping of establishment risk for emerging vector-borne infections: a case study of canine leishmaniasis in southwest France. PLoS ONE 6, e20817.Google Scholar
Hastie, T., Tibshirani, R. and Friedman, J. (2001). Model assessment and selection. In The Elements of Statistical Learning; Data Mining, Inference and Prediction, pp. 214–217. Springer, New York, USA.Google Scholar
Hay, S. I., Guerra, C. A., Gething, P. W., Patil, A. P., Tatem, A. J., Noor, A. M., Kabaria, C. W., Manh, B. H., Elvazar, I. R., Brooker, S., Smith, D. L., Moyeed, R. A. and Snow, R. W. (2009). A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS Med 6, e1000048.CrossRefGoogle ScholarPubMed
Hosmer, D. W. and Lemshow, S. (2000). Applied Logistic Regression. John Wiley & Sons, New York, USA.Google Scholar
Kovats, R. S., Campbell-Lendrum, D. H., McMichael, A. J., Woodward, A. and Cox, J. S. (2001). Early effects of climate change: do they include changes in vector-borne disease? Philosophical Transactions of the Royal Society of London, B 356, 1057–1068.Google Scholar
Lachaud, L., Chabbert, E., Dubessay, P., Dereure, J., Lamothe, J., Dedet, J.-P. and Bastien, P. (2002). Value of two PCR methods for the diagnosis of canine visceral leishmaniasis and the detection of asymptomatic carriers. Parasitology 125, 197–207.Google Scholar
Lanotte, G., Rioux, J.-A., Croset, H. and Vollhardt, Y. (1978). Ecology of leishmaniasis in southern France. 9. Sampling methods in the study and analysis of canine enzootic leishmaniasis. Annales de Parasitologie Humaine et Comparée 53, 33–45.Google ScholarPubMed
Mahamdallie, S. S., Pesson, B. and Ready, P. D. (2011). Multiple genetic divergences and population expansions of a Mediterranean sandfly, Phlebotomus ariasi, in Europe during the Pleistocene glacial cycles. Heredity 106, 714–726.Google Scholar
Maia, C. and Campino, L. (2008). Methods for diagnosis of canine leishmaniasis and immune response to infection. Veterinary Parasitology 158, 274–287.Google Scholar
Maroli, M., Rossi, L., Baldelli, R., Capelli, G., Ferroglio, E., Genchi, C., Gramiccia, M., Mortarino, M., Pietrobelli, M. and Gradoni, L. (2008). The northward spread of leishmaniasis in Italy: evidence from retrospective and ongoing studies on the canine reservoir and phlebotomine vectors. Tropical Medicine & International Health 13, 256–264.Google Scholar
Martín-Sánchez, J., Morales-Yuste, M., Acedo-Sánchez, C., Barón, S., Diaz, V. and Morillas-Marquez, F. (2009). Canine leishmaniasis in southeastern Spain. Emerging Infectious Diseases 15, 795–798.Google Scholar
New, M., Lister, D., Hulme, M. and Makin, I. (2002). A high-resolution data set of surface climate over global land areas. Climate Research 21, 1–25.CrossRefGoogle Scholar
Pullan, R. L., Bethony, J. M., Geiger, S. M., Cundill, B., Correa-Oliveira, R., Quinnell, R. J. and Brooker, S. (2008). Human helminth co-infection: analysis of spatial patterns and risk factors in a Brazilian community. PLoS Neglected Tropical Diseases 2, e352.Google Scholar
Quinnell, R. J. and Courtenay, O. (2009). Transmission, reservoir hosts and control of zoonotic visceral leishmaniasis. Parasitology 136, 1915–1934.Google Scholar
Rabe-Hesketh, S. and Everitt, B. (2004). A Handbook of Statistical Analyses Using Stata, 3rd Edn. Chapman & Hall/CRC, Boca Raton, FL, USA.Google Scholar
Ready, P. D. (2008). Leishmaniasis emergence and climate change. In Climate Change: Impact on the Epidemiology and Control of Animal Diseases (ed. De la Roque, S.) Revue Scientifique et Technique, Office International des Épizooties/ Scientific and Technical Review, World Organization for Animal Health 27, 399–412.Google Scholar
Ready, P. D. (2010). Leishmaniasis emergence in Europe. Eurosurveillance 15, e19505.Google Scholar
Ribeiro, P. J. Jr, and Diggle, P. J. (2001). geoR: a package for geostatistical analysis. R-NEWS 1, 15–18.Google Scholar
Rioux, J. A. and Golvan, Y. J. (1969). Épideémiologie des leishmanioses dans le sud de la France. Institut National de la Santé et de la Recherche Médicale, Paris.Google Scholar
Rogers, W. H. (1993). Regression standard errors in clustered samples. Stata Technical Bulletin 13, 19–23. (Available: http://www.stata.com/products/stb/journals/stb13.pdf. Accessed 11 October 2009)Google Scholar
Rogers, D. J., Hay, S. I., and Packer, M. J. (1996). Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data. Annals of Tropical Medicine and Parasitology 90, 225–241.Google Scholar
Rogers, D. J. and Randolph, S. E. (2006). Climate change and vector-borne diseases. In Global Mapping of Infectious Diseases: Methods, Examples and Emerging Applications (ed. Hay, S. I., Graham, A. J. and Rogers, D.J.), pp. 345–381. Academic Press, London, UK.Google Scholar
Romero, G. A. and Boelaert, M. (2010). Control of visceral leishmaniasis in Latin America – a systematic review. PLoS Neglected Tropical Diseases 4, e584.Google Scholar
Royston, P., Ambler, G. and Sauerbrei, W. (1999). The use of fractional polynomials to model continuous risk variables in epidemiology. International Journal of Epidemiology 28, 964–974.CrossRefGoogle ScholarPubMed
Scharlemann, J. P., Benz, D., Hay, S. I., Purse, B. V., Tatem, A. J., Wint, G. R. and Rogers, D. J. (2008). Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data. PLoS One 3, e1408.Google Scholar
Thomson, M. C., Elnaiem, D. A., Ashford, R. W. and Connor, S. J. (1999). Towards a kala azar risk map for Sudan: mapping the potential distribution of Phlebotomus orientalis using digital data of environmental variables. Tropical Medicine & International Health 4, 105–113.CrossRefGoogle ScholarPubMed
Tibshirani, R., Hastie, T., Narasimhan, B. and Chu, G. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Science,s USA 99, 6567–6572.Google Scholar
Trotz-Williams, L. A. and Trees, A. J. (2003). Systematic review of the distribution of the major vector-borne parasitic infections in dogs and cats in Europe. Veterinary Record 152, 97–105.Google Scholar